Understanding speech in noisy environments is a challenge for normal-hearing and impaired-hearing listeners alike. However, it has been shown that speech intelligibility can be improved in these situations using a strategy called the ideal binary mask. Because this approach requires knowledge of the speech and noise signals separately though, it is ill-suited for practical applications. To address this, many algorithms are being designed to approximate the ideal binary mask strategy. Inevitably though, these algorithms make errors, and the implications of these errors are not well-understood. The main contributions of this thesis are to introduce a new framework for investigating binary masking algorithms and to present listener studies that use this framework to illustrate how certain types of algorithm errors can affect speech recognition outcomes with both normal-hearing listeners and cochlear implant recipients.